Bibliographic record
Abstract
In their recent guidelines for the peri-operative management of obese patients, the authors recommend lean body weight as one of the weight scalars that are useful when estimating drug doses 1. By definition, a weight scalar must not only be proportional to the desired doses, but also be equal to total body weight for non-obese patients. Lean body weight is less than total body weight for all patients, including those of normal weight, because it is total body weight minus the fat mass. To avoid underdosing, lean body weight must be scaled upwards before it can be used as a weight scalar 2, 3. This can be accomplished by normalising lean body weight to ideal body weight using a factor of about 125% for men and 150% for women 4. The resulting weight scalar is proportional to lean body weight for patients of all weights and heights, and is easily calculated or estimated given total body weight and body mass index. The authors of the guidelines 1 also recommend calculating and recording useful quantities including body mass index and weight scalars in order to aid in the management of obese patients. A mobile app for smart phones and tablets that does just that can be downloaded free (as BigSleep) for iOS (Apple Inc., Cupertino, CA, USA) and Android (Google, Mountain View, CA, USA) devices, and is also available as a web application 5. It performs the calculations and then remembers the values throughout the procedure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".